On this episode, Gus talks with Jeff Prince, Professor of Business Economics at Indiana University's Kelley School of Business. Together they examine topics from multiple papers written by Jeff including his most recent work, "Do People Around the World Care Whether Their Data Are Stored Locally". Later in the episode, they discuss Jeff's time at the FCC, where he previously served as the Chief Economist.
Follow Gus Hurwitz on Twitter @GusHurwitz
Links
How Much is Privacy Worth Around the World and Across Platforms | Jeff Prince and Scott Wallsten
Nebraska Governance and Technology Center
On this episode, Gus talks with Jeff Prince, Professor of Business Economics at Indiana University's Kelley School of Business. Together they examine topics from multiple papers written by Jeff including his most recent work, "Do People Around the World Care Whether Their Data Are Stored Locally". Later in the episode, they discuss Jeff's time at the FCC, where he previously served as the Chief Economist.
Follow Gus Hurwitz on Twitter @GusHurwitz
Links
How Much is Privacy Worth Around the World and Across Platforms | Jeff Prince and Scott Wallsten
Nebraska Governance and Technology Center
Disclaimer: This transcript is auto-generated and has not been thoroughly reviewed for completeness or accuracy.
[00:00:00] Gus Herwitz: Welcome to Tech Refactored, a podcast in which we explore the ever changing relationship between technology, society, and the law. I'm your host, Gus Herwitz, the Menard Director of the Nebraska Governance and Technology Center.
Today I'm going to be speaking with Jeff Prince.
[00:00:27] Jeff Prince: My name is Jeff Prince. I am a professor at the Kelly School of Business
[00:00:32] Gus Herwitz: in addition to being a professor of business economics. Jeff spent a year at the Federal Communications Commission as the chief Economist. In our discussion today, we'll talk a little bit about his time as the FCC Chief Economist and the work that he did at the Federal Communications Commission and the role of industrial organization economists; generally, what they do and what they research. But our main focus is going to be his ongoing research on the question of Do People Around the World Care Whether Their Data are Stored Locally? In this project, [00:01:00] Jeff, along with a co-author Scott Walston, are looking at data localization laws around the world.
These are laws that, for instance, might require internet based companies to store their users' information locally within a country or a region. And not export it or transfer to other countries for storage or processing. And in their current research, they're looking at whether users actually care about whether or not companies keep their data locally or transfer it from, for instance, the European Union to the United States, or even to China or Russia.
It's a fascinating paper and I'm looking forward to talking to Jeff about it.
[00:01:40] Gus Herwitz: So we're going to spend some time today. Talking about your recent paper or your current working paper with Scott Walston. Can you tell me a bit about the prior paper with Scott and what that paper did and perhaps most important for this discussion, what your conclusions were?
[00:01:55] Jeff Prince: Yeah, so the prior paper was looking at various data types. Now I'm [00:02:00] gonna have to, you know, call on my memory. It's been a few years. So we had different platforms. We had people's banks, uh, their smartphone, Phone carrier, and then I believe it was Facebook were the four platforms we considered. And then we had various data types on those platforms. So things like your bank balance, biometric information, your fingerprint, your voice print, various other data types, and then all your contacts for Facebook.
And then what we did was try to evaluate what people are willing to accept to give up that information to the companies to share with third parties. And then we compared those across countries and I believe the countries were, uh, the United States, Germany, Columbia, Argentina, Mexico, and- blanking on the sixth one.
So we do that comparison in one thing we find is, you know, the relative valuations for the different data types were strikingly consistent across these countries, um, in quite different countries. So there is a lot of consistency there. The one that stood out as probably as not surprising is [00:03:00] Germany, particularly with financial information.
That was the one I would say rough outlier amongst the group in terms of data type and country. And so the way we frame this is to point out that, you know, there's a lot of different laws being proposed or enacted across these different countries. One thing is just to have a sense of, you know, how do different countries feel about these things in terms of their citizens?
If there's a wide variation, then a wide variation of the laws may be called for. If there's a lot of similarities, then similarities in the laws may be called for.
[00:03:28] Gus Herwitz: And it sounds like those laws also should possibly be treating different types of data differently.
[00:03:33] Jeff Prince: Correct, yes. And, and the ones that stood out were financial information and to some extent biometrics as well.
So again, perhaps not surprising, but in some. You know, I, I think it does add another kind of insight to it because if, if you look at the older literature on these types of evaluations, a lot of times location was the one that was pointed out. So there was a lot of concern about, you know, companies knowing where you were, and then people, you know, [00:04:00] being averse to that.
That is no longer the case. So the lowest one on our list was location and our logic there was. I think people have seen a lot of benefits from that, right? So if they know your location, then the Uber driver knows where to pick you up, right? You can get that, you know, local information easily and quickly.
Mm-hmm. . So I think there's an evolution in what kind of people's version of what the trade offs would be in terms of giving up information versus the benefits that may come along with that.
[00:04:28] Gus Herwitz: Did you look at just instantaneous, my current location, or did that include historical location? Did you differentiate between those in the paper?
[00:04:35] Jeff Prince: Oh, that's a good question. So we-
[00:04:38] Gus Herwitz: I know I'm asking you about this paper that you wrote three or four years ago, and...
[00:04:41] Jeff Prince: Uh, so I, I don't think we spelled that out specifically, but I think it could reasonably be inferred from the way we described it, because I believe the way we described it was, you know, the company has your location data.
So for example, the smartphone, smartphone has location information for you. They could then share that with third parties. I [00:05:00] think it's fair to assume that people were thinking of that as, you know, if they were a senior location over time, they would have that data over time. So there was no implication that like data would be destroyed after a day or something like that.
Mm-hmm. Right.
[00:05:11] Gus Herwitz: And this next question I expect will translate to the more recent paper as well. Your, your methodology was survey based, right? Yes. So you sent around surveys. The, the simple naive question I was going to ask is, it's my understanding that they don't speak English in all these countries, . Um, how, how did you go about actually presenting the survey to individuals in all these different countries in a way that put the questions and the issues on equal terms.
Right. Um, and beyond that, can you just tell me a little bit about the methodology, how you went about conducting these surveys? So a lot
[00:05:44] Jeff Prince: to lot to lay out there, so, Essentially what we've done is in the original one, we wrote the version in English for the US and then Scott actually went down to Mexico City and met in person with focus groups.
I was, I was there virtually, so I was watching [00:06:00] this as well. That was helpful cause then we got some live conversation with people, uh, to get feedback on the way we were describing things, how they were understanding the questions. Um, we took that feedback, edited the survey, and then from. We solicited various translators to take the English version and basically make sure that it was kind of the equivalent information being conveyed in the other countries.
So in the other ones, the main one was translating into Spanish, but then we also had German and all Brazil. That was the one I forgot. And then Portuguese. And so I have to give credit to Scott. He, he found various translators to make that happen. And then this time, same deal. What we did was, I solicited a focus group company where we did this virtually.
So they had individuals come in, go through the survey, we'd have ask them follow up questions. And so then I reviewed all that to make sure that everybody seemed to understand what was going on. And then we did translations again, and this time it was a wider range. So we had Japan, South Korea. Who else did we look?
Italy. [00:07:00] Let's see. We had uk, India. Those did not have to be translated. And so all France. And so Scott took the reigns on that. He had native speakers evaluate surveys, and then the, the basic idea behind the surveys is to use what's what's known as choice-based conjoint. And so the value in that approach, which is become really the predominant method, is that it has people make choices in a similar way that they normally would make choices in the market.
I think a great comparison point I have in my head. If you went to shop for an iPhone on apple's, What you'll typically see is several columns of different iPhone options. Mm-hmm. . And then in the rows there'll be different features and you'll see how they vary on the features. Mm-hmm. And then you can assess, Okay, which one kind of fits my needs or wants the best.
These surveys are designed the exact same way. Mm-hmm. , so you'd have various options. And then the rows would be different features that vary in this case would be types of data sharing. And then on top of that, we would have monthly payments. So, mm. Here's the way your data would be shared, [00:08:00] and then here's what you get paid for those instances, and then tell us which one you would prefer the most.
Right?
[00:08:05] Gus Herwitz: So if you were to just ask someone how much would you be willing to pay, or how much would you want to pay? And different people might interpret those as the same question, or not. For an iPhone, I'd want to pay $2 for an iPhone. Excellent. I would love to pay $2 for an iPhone. Well, that's not useful data.
right? You need to have that, that trade off that comparison. Option A versus option B, Which do you prefer?
[00:08:25] Jeff Prince: That's right. People don't put a lot of stock in these kind of direct solicitation methods. You know what, What's X worth to you, Right? Mm-hmm. . Well, you know, a lot of people are gonna think about that differently, but when you kind of do it indirectly and say, Well, of these things, which would you prefer?
Right? Which one looks the best to you? Mm-hmm. people are very familiar with this idea of making the trade offs right? And so in my experience, these surveys are very good at pulling out how people make trade offs. Mm.
[00:08:50] Gus Herwitz: So there's a a lot of discussion around the world nowadays about privacy and also more generally regulating big tech and technology platforms.
I [00:09:00] wonder in this survey, since it was an international audience that you were targeting or several different countries, were you seeing or controlling for what? I'll just phrases anti-American sentiment and not so much we don't like America, but concerned about we don't like all these American c.
Dominate the big tech space in, in Europe today in particular, there's a lot of discussion and effort to create a European digital economy, um, and certainly privacy concerns. Going back to, uh, Edwards Snowden about American firms and the American government's ability to control American firms that have access to European data, how did you see or take into consideration any of those sort concerns?
[00:09:42] Jeff Prince: That's a great question. I, uh, I would say, We didn't really attempt to control for that. I, I would say, at least from my perspective on that we were allowing for that to be baked into their preferences. So, you know, one thing that potentially of interest that could come out of this is if you saw, say [00:10:00] lots of differences between other countries besides the US and the US in say their preference for localization.
One possible read to that is that the other countries just don't trust the US right now. I'm not sure if that would necessarily play out because there can also be domestic, uh, you know, distrust, uh, because of what happened. But yeah, I, I think that is a potential thing that could come out of this is that, as we mentioned in the paper, One of the cited impetus behind some of these localization laws is exactly the concern about government surveillance, in particular by the United States because of the information that came out, I believe in 2015.
Mm-hmm. . Um, and so that, that is an element to this, but then what our, what our analysis is doing is hopefully giving us a sense as to whether kind of the average citizen. Might be thinking in those terms, right? So if, if the average citizen is showing a strong preference for localization, one possible reason is because they have a, a mistrust in what international countries, possibly the us among them, [00:11:00] um, would be doing with those data.
[00:11:01] Gus Herwitz: So I kind of jumped the gun in transitioning us to the, the new paper, um, which is focusing specifically on that localization question. Can you. Explain what data localization is and the various flavors that it comes in.
[00:11:14] Jeff Prince: Sure. So I think the typical way people think of data localization is that rules in place that would mandate that data be stored domestically and not be shared internationally.
And then there are some various flavors on that. So in other cases, it's. That there has to at least be a copy of the data domestically, so it can be shared internationally, but there has to be a copy domestically. Others might put taxes on data that gets shared internationally. Others might impose rules that the subjects have to give consent in order for the data to be shared inter internationally.
Now, in our case, we're focused on the more extreme version, the one where it's, you know, a rule. You would have to store it domestically and only domestically. Uh, which isn't unusual that that is the case in, in various countries already and being considered by others, but there are different [00:12:00] flavors. Yeah.
[00:12:00] Gus Herwitz: So listeners might remember not too long ago, we had Peter Swire on and we were talking about his work, looking at the European Union. And in the European Union, they've got both strong privacy values, constitutional privacy values and data localization roles, and. Turns out that those privacy values and localization rules can create conflicts for cybersecurity practices.
And that's what we're talking about with Peter. And that's the sort of setting where the data localization rule is saying The data has to stay local. You cannot move it from one jurisdiction to another jurisdiction. Absent some consent or sometimes not even with consent. And that's very much going towards these government surveillance sort of concerns.
We don't trust other governments, in particular, the United States government not to make use of this data that would violate our privacy values. Mm-hmm. so that, that's one aspect of it. And you and I were talking earlier about just the, the wide range of permutations and some of these data localization rules, they're [00:13:00] geared towards making sure that the local.
Has access to this data, right? Perhaps for their own surveillance purposes, or to promote the development of a local industry, or even one of my favorite to teach purposes. It's in many ways very scary, But a lot of countries are now having requirements not just for data localization, but for human localization.
They want employees of companies on the ground so that if a. International company violates a domestic law. There are assets and individuals that they can impound and actually put in jail and go after. Uh, so the, these localization requirements serve a wide range of purposes.
[00:13:40] Jeff Prince: Yeah, absolutely. And to your point, the, you know, one of the.
Reasons is for various types of government administration, law enforcement, those kinds of things that the concern would be if it's not, if there aren't some sort of localization requirements, then the date are all just somewhere else. And then there's no way for the government to be able to monitor whether you're kind of obeying various [00:14:00] laws or, you know, be able to execute some of their program.
[00:14:01] Gus Herwitz: So in this iteration of the, the project, you did another survey based methodology. Yep. How, how were the surveys different from the previous.
[00:14:11] Jeff Prince: In a couple dimensions, the, the most important one is it allows for a localization variation in what people are giving up in terms of their data. So we actually did it in multiple ways.
So in short, we basically had four different possibilities for your data. So just to give a concrete example of if you think about your smartphone and there's location data, as we were talking about before in the previous paper, we. Talk about could they share that data with other third parties or not.
Now what we do is allow for. They can't share it at all. That's one possibility. But now when sharing occurs, it's either domestic only, domestic and international, but excluding China and Russia and then domestic and international, No barriers. Mm-hmm. . And so we vary it along those different dimensions and get a sense for, you know, how much do [00:15:00] people care about any kind of differences along those lines.
[00:15:02] Gus Herwitz: I wonder. If you were to ask me, would you accept this plan for $20 or would you accept this plan, which won't share your data with China and Russia for $19? I don't know what the number is. Wait, what? Why? Why are you mentioning China and Russia? ? The, the presentation of that is an option would very much influence.
Understanding both of that option and the other options because wait, why are, why are China and Russia involved? Is that something that you took into account in this?
[00:15:34] Jeff Prince: So, it's a great question. So as I said, I listened in on basically all of the focus groups, questionings, and we didn't get a lot of people kind of have their heritage raised on that issue.
And what I think is particularly interesting, you know, jumping the gun on the results a little bit, at least thus far, That doesn't seem to have made much difference in people's minds. So it doesn't appear that [00:16:00] singling out China and Russia really did much. And if anything, we found that people would rather share their data with everybody than exclude China and Russia.
And at first, you know, we, we talked about this and it was like, does that make any sense? How, what would be the reasons for. But the more I thought about it, it, it might make a lot of sense. And, you know, one example, I was talking to somebody recently about this, and, uh, they mentioned, uh, TikTok, right?
Mm-hmm. . So it, it comes back to this idea that when you put some of these restrictions on, Yeah, there are potentially benefits, but there also could be costs to you. And so it's not unreasonable to think that some people looked and said, Oh, China would be excluded. Oh, does that mean that TikTok won't be as good?
Mm-hmm. , Oh, then maybe I don't really wanna do that. I'd rather just let the cat outta the bag and let everybody have it. The other thing that came up in the focus groups that I thought was interesting is people readily kind of got it with regard to China and Russia, that essentially, okay, you're excluding China and Russia.[00:17:00]
Iran's in there, right? Mm-hmm. , you know, all these other C. Are in this group. Well, okay, once it's kind of out, I, who knows what's gonna happen once some of these countries get ahold of it. So it may not be that surprising that excluding those two countries didn't make much difference in people's minds.
[00:17:16] Gus Herwitz: That's really interesting. And I, I wonder, I 100% recognize I live in a bubble. I expect you live in a bubble as well. Many academics, most academics who work in this space, one of the jokes I think it might be that folks say is we're all national security lawyers nowadays. Us are lawyers or national security scholars.
Because if you do industrial organization or competition policy or anything involving the tech industry over the last five, 10 years, it's been remarkable how national security has come to the forefront in almost every discussion. Yeah. Um, and I expect for. Most Americans and most users around the world, there might be some general [00:18:00] awareness.
I heard something about TikTok and China's- I, I don't know what's going on there. Um, but for some of us, oh, TikTok China, this is a Chinese controlled company. We're concerned about Chinese government using it to access data or to influence elections or whatever. And it's a fascinating bit of data that if ordinary users aren't that concerned, I don't know what we should make of that, but it's a fascinating data point.
[00:18:24] Jeff Prince: No, it is. It's, uh, like you said, it's an ongoing issue. I mean, when I was at the FCC, one of the primary things that people were talking about was Huawei, right? Mm-hmm. , so, They were putting bands on using Huawei technology for government supported projects and exactly because it was a national security concern, right?
You know, China owned company, and now there's concern about what's in those products and you know, what kind of data are they able to collect? So, yeah, it's, it's been top of mind for a while.
[00:18:52] Gus Herwitz: Yeah. So I, I'd love to turn to your time at the FCC in a moment. We, we should, uh, get what are the results of your paper?
[00:19:00] What are you finding at this point?
[00:19:02] Jeff Prince: So, I, I absolutely have to preface this, what these are extremely preliminary because, uh, I mean, these are hot off the press and we actually still have a few countries to go. But, you know, as I alluded to before, I, I think, at least to this point, and I should be clear right now, we have results that I can share from the United States, the.
Kingdom, France and India. And the general takeaways that I've taken so far is that the relative valuations that I talked about in the previous paper seems to hold up pretty well here as well in that. The ordering in terms of countries, how much they value different types of data seems to be quite consistent.
So that I, I thought was quite interesting. And then I guess the other thing to, to highlight is the people's concern about localization seems largely to be not that great where there is concern, where there seems to be actual, you know, noticeable differences and people's willingness to accept is exactly on the types of data where.
They seem to worry about those data in general. And in this case, the data that [00:20:00] stood out, uh, at least from what I've seen thus far is kind of basic personal information like home address and home phone number, and then financial and biometric data again.
[00:20:09] Gus Herwitz: I I love that home address and phone number information that very often already is public.
[00:20:13] Jeff Prince: It, it is, It's, yeah, I- it is. It's amazing. And, and it's funny because this is where doing the focus groups is very helpful because you actually get to talk, you know, really see what people are thinking. And so I was able to anticipate that result because many times during the focus groups, people specifically pointed to things like their phone number.
That they just didn't want out there. Mm-hmm. , which I agree is funny cuz it generally is, but- but there was, you know, very, not with none of my involvement, this was being independently brought up by focus group participants.
[00:20:46] Gus Herwitz: That's fascinating. So I, I know this is still early work. The, the results aren't all in yet, but how do you translate this to policy?
Do you have either thoughts generally on how work like this. Can or should be translated into [00:21:00] policy or the specific results that you're anticipating, what they might tell us that we should be thinking about?
[00:21:05] Jeff Prince: Yeah, I guess I, I look at it as it's, um, Our hope has always been that it would be a, a component of the debate, right?
That as we cite in the paper, there's, there's various reasons why you may or may not want to go the route of data localization laws. And so this is contributing to knowledge on one of those dimensions. So, Do the citizens seem to care about this? Do? Is there a welfare gain from the citizens standpoint in putting a localization law in place?
And I think, you know, at least these early results suggest that the welfare gains for a lot of these data types may be quite small. Mm-hmm. . Whereas there might be a few where they are non-trivial, you know, like the, the biometrics and the, the financial information. And so it says a, you know, Pointing to citizen, getting more utility for your citizens or improving citizen welfare may be overstated in some cases.
B, that there might be reason to [00:22:00] have variation in what the localization rules would be according to the data type. And I think that's already been tried in some countries. There are, I think India and some others have specific types of data that need to be localized, but not all. And this points. There might be reasons to do that from the point of view of citizens and their utility.
That would be kind of the initial takeaways. We'll see if it all holds up. I mean, the other thing that I noticed is, uh, just from this small sample, once again, Europe stands out. Mm-hmm. , uh, there seems to be, in terms of their evaluation of privacy in general, France and UK seemed to be higher than the US and India in most of our metrics.
France in particular, uh, UK was closer to being on par with the us mm-hmm. , um, but France in particular, boy, their, their numbers were substantially higher. Yeah. That's fascinating.
[00:22:45] Gus Herwitz: Um, I, I was going to make the, the observation. One of the things that we, I'm a law professor and I, I teach and do some work relating to privacy and one of the things that we're always talking about is the two cultures of privacy.
The, the American versus the [00:23:00] European understandings of privacy and privacy values. And to the extent that your work is showing that citizens around the world tend to have very similar views on privacy. That's running counter to that narrative. But to the extent that it is showing that in European Union countries, they do have stronger view.
perhaps it should refine the general understanding, not American versus European privacy values, but global privacy values versus European privacy values.
[00:23:28] Jeff Prince: Yeah. And I, I will hopefully have a lot more to say on that with Japan and South Korea as well. Mm-hmm. . Right. Um, but that's right, and we already have some Latin American countries from the previous study, so Yeah, we're, we're getting a, a much broader picture, I think from this than perhaps we had in the past.
[00:23:44] Gus Herwitz: I want to spend some time just talking about you. Oh, good. All right. So, uh, you, you, you mentioned before, uh, during the introduction, you're a, uh, professor at, uh, Indiana Kelly, and you spend some time at the FCC as a, Actually, I guess that the position is the chief economist. Yes. Um, [00:24:00] which, I think to outsiders, chief economist, that sounds like you're the, the, the boss of the place.
Right. Um, can you tell us a bit about, uh, what the chief economist of the FCC is and what that role is like?
[00:24:10] Jeff Prince: Yes. So it's, I can't give the full history of it. I can, I can give more of a, a sense of what, at least it has certainly evolved to be when I was there, by definition, the chief economist is an advisor to the chairman, and so ultimately that's what the position entails.
But practically speaking, at least in my experience, it was. Bringing an academic outsider's perspective on a wide range of issues that the FCC was dealing with. So I found it to be fantastic because, so you're not running the office. So they, when I got there, they had reorganized the commission where they had developed this new office of economics and analytics.
[00:24:47] Gus Herwitz: And, and I should say, uh, I, I just jumped right into FCC, the Federal Communications Commission, the Washington DC Federal Agency that administers our federal communications.
[00:24:58] Jeff Prince: That's right. Uh, [00:25:00] and when I was there, the, the chairman was a, a Jeep Pie, so the office that I was housed in was the new Office of Economics and Analytics.
So they have their own office chiefs, you know, their own infrastructure, the, where that's run. And so the, the Chief Economist kind of fits in at the top level, working with the top people in the, in the office. But then I, I was able to distribute myself across different projects. So they'd have different things going on simultaneously.
Get involved with 2, 3, 4, 5 things at once, and I, I found it very gratifying because they give you a lot of freedom. It really was. What are the projects that are most interest to you, where you think you can contribute the most? Get on it. And they, to their credit, they really did listen to the ideas that I had to offer.
Um, I felt like I was really deeply involved with several of the projects.
[00:25:46] Gus Herwitz: And, you know, the commission, uh, works on a, a vast range of projects, I think for many outsiders and main people listening, uh, to this discussion, probably. FCC, what, what do they do? They, they do net [00:26:00] neutrality. Right? That's the only thing that they're known for.
Um, and, uh, for either doing or not doing net neutrality. Okay. Can you, uh, give us just a sampler platter of some of the things that you got to see the commission working on?
[00:26:11] Jeff Prince: Yeah, and I should say up front, I was glad that the net neutrality craziness had kind of been passed. When I got there, it was, I had heard it was pretty nuts.
Um, so yes, the net neutrality component is one of them, but yes, there's much more, probably the one that. Financially is the most impactful, is the many auctions that they run involving distribution of universal service funds. So they collect quite a bit of money from telecom providers. Um, which ultimately is coming from consumers, but that money then is collected and, and used to help facilitate provision of service right.
In areas that, you know, the incentives typically are not as good. So trying to get universal service, which originally was to do with telephone, but now has really evolved to be thinking in terms of broadband internet. Mm-hmm. . Um, and so they distribute those [00:27:00] funds and I think interestingly they use these various auction methods to do that.
And essentially what it is, is providers will come in and. All provide service to area X. For why amount of money. Right. And whoever will do it for the least amount of money, they get it right. As long as they fulfill the promises of what provision would be. And interestingly, those, those auctions that the FCC uses, essentially Paul Milgram, who was one of the Nobel Prize winners recently, uh, for auction design, they were utilizing a lot of his, his methods.
And so that was back in the early nineties where that kind of really started. Mm-hmm. , um, So that's a, a big component of it. Spectrum is another one. So they, they manage a lot of, uh, spectrum allocation. So a big deal when I was there was they were freeing up what was known as C band. Um, it was currently being occupied by a bunch of satellite companies.
And so you can imagine the, the legal wranglings, uh, to try and free
[00:27:56] Gus Herwitz: that up. Yeah, so the, the FCC was basically trying to [00:28:00] take back some of the spectrum that satellite providers use, uh, in order to re apportion it to be used for, uh, cell
[00:28:06] Jeff Prince: phone service. Correct. And, and again, that would ultimately be auctioned off, right?
Mm-hmm. . Um, and so then, you know that money goes into the treasury, but accomplishing that, I think what was really interesting about that particular situation, I believe, 3.7 to 4.2 was the range. And they were gonna get these guys to condense it to where they only had 0.2 and they'd free up 0.3. But at the time, de facto, my understanding was that all there were five satellite companies.
Mm-hmm. , and they all had essentially rights to the full 0.5. Mm-hmm. . And so you couldn. Whole one of them, all five of them had to be negotiated with and kind of simultaneity because they were all, you know, property rights holders. At least the understanding seemed to be that they were property rights holders of the full range.
So that, that created some, some thorny negotiations, no doubt. Mm-hmm. . [00:29:00] Yep. Those are big ones. Um, you know, rolling out broadband service spectrum allocation. Um, but then there's. Other things that people might think of in terms of just, uh, regulating markets. So some markets have, you know, market failure issues.
The one, um, that I was most involved with was inmate calling systems. So there you've got like a prison will provide telecom services to the inmates that's gonna be provided by some telecom company. , they give that right to just one company. And so then of course the problem then results from that is then you've got a monopolist.
Mm-hmm. ultimately that is gonna have a lot of price power in a situation like that. And so to try and manage it so there's not, you know, too much exercise of market power there. You can have the government come in and, and put rate caps in mm-hmm. . And so we were working really hard to figure out what would be reasonable rate caps in a circumstance like that.
So
[00:29:52] Gus Herwitz: a wide range of services. Which brings me to the other question about who are you, ? You're, you're a business professor, you're [00:30:00] an io, an industrial organization economist, and you are at the, uh, Federal Communications Commission working on spectrum issues and broadband internet service and. Prisons and inmates.
And what's an IO economist .
[00:30:15] Jeff Prince: Uh, so broadly speaking though, I always describe it is we're, we're modeling how firms compete, we're modeling demand systems, and then ultimately thinking about what are the determining factors for market outcomes. You know, the classic. Economics as simple as drawing a supply and, and demand curve.
Right. And just look where they intersect. In a sense that's, that's what we're doing, but just thinking hard about what are the real elements to those, those components. Mm-hmm. . Um, and so, you know, a, a classic thing in an IO economist would do if you think about like the ics that in main calling systems, You could think about what would a competitive marketplace look like?
What kind of prices would emerge in a world like that? What are the elements to competition? If you had two firms, if you had five firms, does it make a difference? What are they competing on? What are, you know, is it just [00:31:00] price? Is it other service features? If so, how so? And then, of course, What's gonna feed into that is what do people care about, right?
So what are the service features that people care about? Which then would incentivize firms either compete on those things, make investments to make improvements on those things. Mm-hmm. . So those are the kinds of things that an IO economist would think about. And ultimately then it's gonna feed into.
Thinking about what would be an alternative competitive world in things like telecom service for a prison. But then also we, we think about cost side matters. Mm-hmm. . So with the ics, we collected cost data and said, Okay, what, what's a reasonable estimate for what, say the marginal costs are for these firms or their average costs so that we can put rates to make sure that they're at least breaking even, but then not, uh, imposing too much pain on the payers, uh, for the services and you.
[00:31:51] Gus Herwitz: Also a professor. Yes. We've been focusing on your, your research and your professional work, but uh, you also trained the next generation of yous and mes out [00:32:00] there, . Um, how has your experience working on things like stuff at the FCC and your research generally changed how you're teaching and also. How is teaching students in the modern era different?
How's it changing compared to when you were a student in terms of substance and what's being taught? Not we, we don't need to talk about zoom and hybrid teaching.
[00:32:23] Jeff Prince: Ah, yes, Yes. In some ways I think, uh, the answer to those questions go hand in hand. I, I would say that, you know, my experiences like the FCC and just my own research, you know, doing consulting work as well, it allows me to really bring in.
A deeper level of application to what I'm teaching. Um, so for example, a lot of what I've taught in the recent past has been econometrics base. So using statistics to do economic analysis that can get abstract quickly. So you can talk about central limit theorem, you can, you know, get into limiting principles and all these things.
But [00:33:00] you know, ultimately I think especially in a business school, The students are interested in where is this being used? How is this actually gonna change what a firm does? Or how is this gonna help me to be a better manager? And then where else? My department also is public policy. So some of our students go into policy jobs.
And so, how's this gonna make me a better regulator? How's this gonna make me a better policy maker? And, uh, it really helps me to bring in examples so I can say this really made the difference. Right. You know, some of the stuff that we did at the fcc, Utilized methods that we're talking about right here, right now.
Mm-hmm. . Um, and that ultimately is what was driving some of the decisions that were made and affects real people. I, I enjoy that a lot. I think that gets students attention a lot more mm-hmm. , um, than when you just have to use kind of abstract examples to demonstrate the point. And I would say, you know, what's changed since I was in school, I do think there's a stronger demand.
I, I, I've been teaching MBAs recently, so some of this might be biased by just MBA versus undergrads, but I do think there. You know, more and [00:34:00] more interest and want for, where does the rubber hit the road with this stuff, right? Mm-hmm. , how does this actually make a difference? I came from, you know what, I did a math stats in econ undergrad and uh, you know, a lot of it could be abstract and I, I think it was just kinda like, that's fine.
It's abstract. We'll see how it applies later. Especially now with the way I'm teaching and my experience with the students. It is valuable and important to be able to. This is where this stuff actually gets used. Mm-hmm. , this is where it's actually gonna make a difference. And it's
[00:34:29] Gus Herwitz: perplexing in a sense because almost all of the great ideas and the concepts that, uh, you learn in any field, economics, business, certainly law engineering, anything, these ideas, these theories, these really abstract constructs.
They were almost always developed to solve a problem. Yes, Or in response to some real world situation. And sometimes they're then generalized into some more general theory. Other times what students are learning is just a, a very narrow [00:35:00] solution to a, a specific problem. That's a really important problem that we see throughout society.
So we study it a whole lot, but students so often, Removed from the tangible reality of what they're learning, and they're being taught it as, here's this theory that these two people developed, right? And now has their name. And it's like, okay, this is the grand theory that I'm learning. And that's not what most of research is about.
That's not most of what you're learning. Rather, you're learning the tools so that you can apply those tools, but also, Learn to develop your own tools and learn to solve future problems. And one day, one day have a, a theorem named after you and
[00:35:38] Jeff Prince: your best friend. That's right. That's a great point. Uh, especially coming from a math background.
So I was always hardcore math person. I, that was what I was gonna do in my career. And, uh, your point's very well taken. Cause I think especially in math, you'd have like the, like greens theorem, right? And you'd go, what ? I could do it, I could solve the problems. But I think [00:36:00] especially today, and I think it would've helped me back then if there was some context to it where you could say, Yeah, you know, Green came up with a, the, you know, it's a really important insight.
But as you said, odds are. He had something in mind, right? Mm-hmm. that, that this was helping us to solve that has some practical application. Now that's not always the case, but a lot of times it is, At least for me. I think that that would help me to learn things better. And I, in my experience, students appreciate that.
I think it helps, helps make the ideas stick and it also helps motivate going through the pain of learning through some complex ideas. Mm-hmm. , what are you working on next? Oh wow, good question. So, number of projects, I, uh, As we were talking about last night, I, a lot of what I'm interested in is, has some sort of quality dimension, quality competition component to it.
So some of the stuff that I've done along those lines has been in airlines. I've, I've had a lot of good traction that way. Uh, so I have some airlines projects and, you know, one of them that [00:37:00] we've been working on for a while, uh, that I've always enjoyed this project, it's about promises versus. And airlines is a great area to do that because you can think of a schedule time as a promise.
Mm-hmm. , and then the on time performance is delivery. Mm-hmm. . And so what we're looking at is what might be the key forces that push how much of a promise a firm is gonna make, Right? Mm-hmm. . Cause in some, I think we're all familiar, you know, in some industries. It feels like there's consistent over promising.
And in other industries there's under promising. And the examples I have in my head is if you've ever built a house or had involved, gotten involved with construction, they always over promise. Right? Like, it, it's always, we'll get it done by X and then that never happens. Mm-hmm. or the budget will be y and it never plays out.
Mm-hmm. . Um, but on the other hand, if you go to a restaurant right, and they tell you what the wait time will be, at least in my experience, a lot of times, They under promise. Mm-hmm. , they'll tell you it's longer than it actually turns out to be. And then thinking about like what might be the economic forces [00:38:00] behind why it's the case for one and a different case for the other.
And airlines also is interesting because there is a demonstrated change over time that airlines have gone from over promising the under promising over the last 30 years. Mm-hmm. so. What's going on there? Why, why is there such an obvious change? I love,
[00:38:17] Gus Herwitz: as someone who spends far too much time on airplanes, , I've actually ridden a couple of short pieces on this at various points.
Airlines, one of the chief metrics is OTD on time departure. Yep. And they get penalized if, uh, they push back from the gate. A single second late. Um, and that makes no sense. Yeah, because you're, you're stranding passengers and you're probably going to get to the destination five minutes ahead and probably no one is going to care all that much if you're two minutes late.
Anyhow, so why don't we focus on, on time arrivals as the. Relevant metric over, uh, on time departure and the way that we measure what we choose to measure affects the performance that [00:39:00] we get in really fascinating
[00:39:01] Jeff Prince: ways. Oh, absolutely. Uh, yeah, and there's, there's a nice paper, I believe by Forbes and Letterman that demonstrate exactly that.
It's like, You kind of get what you measure. Mm-hmm. , I mean, I think we all know that's true, but to really show it. I mean, they, they have a nice paper that really demonstrates that. Well,
[00:39:16] Gus Herwitz: we are about at the measure of time for our conversation. Uh, it's, uh, been a real pleasure. Likewise. Um, and I look forward to seeing how your paper with Scott continues to develop and what comes next.
[00:39:27] Jeff Prince: Sounds good, appreciate it. Thanks Gus.
[00:39:34] James Fleege: Tech Refactored is part of the Menard Governance and Technology Programming Series hosted by the Nebraska Governance and Technology Center. The NGTC is a partnership led by the College of Law in collaboration with the colleges of Engineering business and journalism and Mass communications at the University of Nebraska Lincoln.
Tech Refactored is hosted and executive produced by Gus Herwitz. James Fleege is our producer. Additional production assistance is provided by the NGTC [00:40:00] staff. You can find supplemental information for this episode at the links provided in the show notes to stay up to date on the latest happenings within the Nebraska Governance and Technology Center.
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